Inferring behavior from functional brain images.
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Brain Mapping↗
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Results for “functional inference”
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A multicompartment vascular model for inferring baseline and functional changes in cerebral oxygen metabolism and arterial dilation.
Functional hemodynamic responses are the composite results of underlying variations in cerebral oxygen consumption and the dilation of arterial vessels after neuronal activity. The development of biophysically based models of the cerebral vasculature allows the separation of the neuro-metabolic and neuro-vascular influences on measurable hemodynamic signals such as functional magnetic resonance imaging or optical imaging. We describe a multicompartment model of the vascular and oxygen transport dynamics associated with stimulus-driven neuronal activation. Our model offers several unique features compared with previous formulations such as the ability to estimate baseline blood flow, volume, and oxygen consumption from functional data. In addition, we introduce a capillary compliance model, arterial and venous oxygen permeability, and model the dynamics of extravascular tissue oxygenation. We apply this model to multimodal optical spectroscopic and laser speckle imaging of the rat somato-sensory cortex during nine conditions of whisker stimulation. By fitting the model using a psuedo-Bayesian framework to incorporate multimodal observations, we estimate baseline blood flow to be 94 (+/-15) mL/100 g min and baseline oxygen consumption to be 6.7 (+/-1.3) mL O(2)/100 g min. We calculate parametric, linear increases in arterial dilation (R(2)=0.96) and CMRO(2) (R(2)=0.87) responses over the nine conditions. Other parameters estimated by the model include vascular transit time and volume reserve, oxygen content, saturation, diffusivity rate constants, and partial pressure of oxygen in the vascular compartments and in the extravascular tissue. Finally, we compare this model to earlier work and find that the multicompartment model more accurately describes the observed oxygenation changes when compared with a single compartment version.
Arteries↗
Real time human brain function: observations and inferences from single trial analysis of magnetoencephalographic signals.
This paper brings together results obtained by applying Magnetic Field Tomography (MFT) to the analysis of Magnetoencephalography (MEG) data over the last decade. It emphasizes the most recent developments where the availability of helmet-like MEG probes with well over 100 sensing coils provide a full coverage of the head. The paper shows that it is possible to extract tomographic information from single trial, millisecond by millisecond MEG signal, and demonstrates two ways that this capability can be exploited. First, the single trial reconstructions are used to obtain robust statistical measures of changes of activity over small latency windows. Second, the interaction between areas is studied by computing the mutual information between short, time-lagged sections of the single trial time-courses. The usefulness of the computationally demanding approach is demonstrated by analyzing experiments using two widely used protocols, one for face and affect recognition and the other for Contingent Magnetic Variation (CMV). The results show foci of significant changes of activity, which are consistent with what is reported in the literature and provide a deeper understanding of their significance. Some new, but not all that unexpected, findings also emerge from the analysis.
Brain↗
Genomic logic, allelic inference, and the functional classification of genes.
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Alleles↗
Saddlepoint distribution function approximations in biostatistical inference.
Applications of saddlepoint approximations to distribution functions are reviewed. Calculations are provided for marginal distributions and conditional distributions. These approximations are applied to problems of testing and generating confidence intervals, particularly in canonical exponential families.
Biomedical Research↗
Models of brain function in neuroimaging.
Inferences about brain function, using neuroimaging data, rest on models of how the data were caused. These models can be quite diverse, ranging from conceptual models of functional anatomy to nonlinear mathematical models of hemodynamics. However, they all have to be internally consistent because they model the same thing. This consistency encompasses many levels of description and places constraints on the statistical models, adopted for data analysis, and the experimental designs they embody. The aim of this review is to introduce the key models used in imaging neuroscience and how they relate to each other. We start with anatomical models of functional brain architectures, which motivate some of the fundaments of neuroimaging. We then turn to basic statistical models (e.g., the general linear model) used for making classical and Bayesian inferences about where neuronal responses are expressed. By incorporating biophysical constraints, these basic models can be finessed and, in a dynamic setting, rendered causal. This allows us to infer how interactions among brain regions are mediated.
Biophysical Phenomena↗
Modeling other minds.
Nine normal volunteers performed a 'theory of mind' task while their regional brain blood flow pattern was recorded using the PET [15O]H2O technique. Control conditions induced subjects to attend to the visual and semantic attributes of known objects. In a third condition, subjects had to infer the function of an unfamiliar object from its form. In the 'theory of mind' condition, subjects had to infer function based on the form of both familiar and unfamiliar objects and in addition, model the knowledge and rationality of another mind about the function of these objects. Performance during the 'theory of mind' condition evoked the activation of a distributed set of neural networks with prominent activation of the left medial frontal lobe (Brodmann area 9) and left temporal lobe (Brodmann areas 21, 39/19, 38). This result suggests that when inferential reasoning depends on constructing a mental model about the beliefs and intentions of others, the participation of the prefrontal cortex is required. When access to such knowledge is affected by central nervous system dysfunction, such as that found in autism, modeling other minds may prove difficult.
Adolescent↗
Learning gene functional classifications from multiple data types.
In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. In addition, we describe feature scaling methods for further exploiting prior knowledge of heterogeneity by giving each data type different weights.
Algorithms↗
Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome-wide scale.
Many thousands of proteins encoded by the genome of Plasmodium falciparum, the causal organism of the deadliest form of human malaria, are of unknown function. It is of utmost importance that these proteins be characterized if we are to develop combative strategies against malaria based on the biology of the parasite. In an attempt to infer protein function on a genome-wide scale, we computationally modeled the P. falciparum interactome, elucidating local and global functional relationships between gene products. The resulting interaction network, reconstructed by integrating in silico and experimental functional genomics data within a Bayesian framework, covers approximately 68% of the parasite genome and provides functional inferences for more than 2000 uncharacterized proteins, based on their associations. Network reconstruction involved the use of a novel strategy, where we incorporated continuously updated, uniform reference priors in our Bayesian model. This method for generating interaction maps is thus also well suited for application to other genomes, where pre-existing interactome knowledge is sparse. Additionally, we superimposed this map on genomes of three apicomplexan pathogens--Plasmodium yoelii, Toxoplasma gondii, and Cryptosporidium parvum--describing relationships between these organisms based on retained functional linkages. This comparison provided a glimpse of the highly evolved nature of P. falciparum; for instance, a deficit of nearly 26% in terms of predicted interactions is observed against P. yoelii, because of missing ortholog partners in pairs of functionally linked proteins.
Animals↗
Event-related functional magnetic resonance imaging: modelling, inference and optimization.
Event-related functional magnetic resonance imaging is a recent and popular technique for detecting haemodynamic responses to brief stimuli or events. However, the design of event-related experiments requires careful consideration of numerous issues of measurement, modelling and inference. Here we review these issues, with particular emphasis on the use of basis functions within a general linear modelling framework to model and make inferences about the haemodynamic response. With these models in mind, we then consider how the properties of functional magnetic resonance imaging data determine the optimal experimental design for a specific hypothesis, in terms of stimulus ordering and interstimulus interval. Finally, we illustrate various event-related models with examples from recent studies.
Brain↗
On the functional equivalence of fuzzy inference systems and spline-based networks.
The conditions under which spline-based networks are functionally equivalent to the Takagi-Sugeno-model of fuzzy inference are formally established. We consider a generalized form of basis function network whose basis functions are splines. The result admits a wide range of fuzzy membership functions which are commonly encountered in fuzzy systems design. We use the theoretical background of functional equivalence to develop a hybrid fuzzy-spline net for inverse dynamic modeling of a hydraulically driven robot manipulator.
Fuzzy Logic↗
Assessing the accuracy of ancestral protein reconstruction methods.
The phylogenetic inference of ancestral protein sequences is a powerful technique for the study of molecular evolution, but any conclusions drawn from such studies are only as good as the accuracy of the reconstruction method. Every inference method leads to errors in the ancestral protein sequence, resulting in potentially misleading estimates of the ancestral protein's properties. To assess the accuracy of ancestral protein reconstruction methods, we performed computational population evolution simulations featuring near-neutral evolution under purifying selection, speciation, and divergence using an off-lattice protein model where fitness depends on the ability to be stable in a specified target structure. We were thus able to compare the thermodynamic properties of the true ancestral sequences with the properties of "ancestral sequences" inferred by maximum parsimony, maximum likelihood, and Bayesian methods. Surprisingly, we found that methods such as maximum parsimony and maximum likelihood that reconstruct a "best guess" amino acid at each position overestimate thermostability, while a Bayesian method that sometimes chooses less-probable residues from the posterior probability distribution does not. Maximum likelihood and maximum parsimony apparently tend to eliminate variants at a position that are slightly detrimental to structural stability simply because such detrimental variants are less frequent. Other properties of ancestral proteins might be similarly overestimated. This suggests that ancestral reconstruction studies require greater care to come to credible conclusions regarding functional evolution. Inferred functional patterns that mimic reconstruction bias should be reevaluated.
Algorithms↗
Clustering proteins from interaction networks for the prediction of cellular functions.
BACKGROUND: Developing reliable and efficient strategies allowing to infer a function to yet uncharacterized proteins based on interaction networks is of crucial interest in the current context of high-throughput data generation. In this paper, we develop a new algorithm for clustering vertices of a protein-protein interaction network using a density function, providing disjoint classes. RESULTS: Applied to the yeast interaction network, the classes obtained appear to be biological significant. The partitions are then used to make functional predictions for uncharacterized yeast proteins, using an annotation procedure that takes into account the binary interactions between proteins inside the classes. We show that this procedure is able to enhance the performances with respect to previous approaches. Finally, we propose a new annotation for 37 previously uncharacterized yeast proteins. CONCLUSION: We believe that our results represent a significant improvement for the inference of cellular functions, that can be applied to other organism as well as to other type of interaction graph, such as genetic interactions.
Cluster Analysis↗
Statistical limitations in functional neuroimaging. II. Signal detection and statistical inference.
The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low-pass filtering in relation to functional-anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non-inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.
Biometry↗
On Bayesian inference for the K function.
The K function is a summary of spatial dependence in spatial point processes. In practice one observes a realization of the spatial point process, called a spatial point pattern. Although the K function of a spatial point process is typically unknown, several estimators of the process K function have been put forth. These estimators, however, are based upon empirical averages; the complicated distributional properties of the estimators unfortunately complicates interval estimation. In this paper, we propose a Bayesian inferential framework, allowing inference for the K function of the spatial point process (including interval estimation). Of particular interest is the unique use of the posterior predictive distribution to (efficiently) enable such inferences. To demonstrate our technique, the well known Swedish pine sapling data (Strand, 1972) is analyzed, including a discussion on evaluating model fit.
Algorithms↗
Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps.
The statistical inference on functional imaging data is severely complicated by the embedded multiple testing problem. Defining a region of interest (ROI) where the activation is hypothesized a priori helps to circumvent this problem, since in this case the inference is restricted to fewer simultaneous tests, rendering it more sensitive. Cytoarchitectonic maps obtained from postmortem brains provide objective, a priori ROIs that can be used to test anatomically specified hypotheses about the localization of functional activations. We here analyzed three methods for the definition of ROIs based on probabilistic cytoarchitectonic maps. (1) ROIs defined by the volume assigned to a cytoarchitectonic area in the summary map of all areas (maximum probability map, MPM), (2) ROIs based on thresholding the individual probabilistic maps and (3) spherical ROIs build around the cytoarchitectonic center of gravity. The quality with which the thus defined ROIs represented the respective cytoarchitectonic areas as well as their sensitivity for detecting functional activations was subsequently statistically evaluated. Our data showed that the MPM method yields ROIs, which reflect most adequately the underlying anatomical hypotheses. These maps also show a high degree of sensitivity in the statistical analysis. We thus propose the use of MPMs for the definition of ROIs. In combination with thresholding based on the Gaussian random field theory, these ROIs can then be applied to test anatomically specified hypotheses in functional neuroimaging studies.
Artifacts↗
A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics.
Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity. Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.
Journal Article↗
Interactions among neuronal systems assessed with functional neuroimaging.
Cortical organisation is based on the principles of functional specialisation and functional integration. Those two concepts exist at multiple spatial scales. For example at a macroscopic level, functionally segregated areas V1 and V5 are integrated within the dorsal visual stream. At a microscopic level, it is possible to infer the functional integration of (segregated) ocular dominance columns within area V1. At the macroscopic level functional specialisation and integration can be tested with functional neuroimaging, using fMRI/PET or EEG/MEG. A framework that allows making inferences on functional integration between brain regions using fMRI will be presented. The common feature of all techniques within that framework is that they can incorporate second order terms and therefore explicitly allow for contextual modulations. Two examples demonstrate how attention and paired associates learning can modulate effective connectivity within the visual system. Furthermore these models can incorporate non-linear modulatory (feedback) connections that can account for the changes in effective connectivity observed.
Attention↗